A Self-Calibrating Neural Network Model for Real-Time Sensor Drift Correction in Engineering Systems

Maintaining precision throughout the lifetime of an engineering system poses a challenge; calibrating the sensor in real-time can be particularly troublesome because of the need for pauses in productivity. Changes in throughput, aging sensors, or instability of the calibration hardware can all result in drift. In this paper, we propose a Self-Calibrating Neural Network (SCNN) model, which aims to eliminate the manual recalibration requirement, enabling uninterrupted sensor drift correction during system operation. An RNN core is integrated into the SCNN architecture along with an adaptive feedback loop that teaches itself using the incoming sensor data and benchmarks. The model learns to adjust outputs to operational values over time by identifying shifts in sensor offset patterns. Industrial temperature, pressure, and vibration sensors were used to validate the model, applying both artificial and real data sets. The results showed that the SCNN model enhanced accuracy considerably, achieving up to 85% reduction in drift correction error compared to existing frameworks. The innovative feature of the adaptive feedback loop offers sustained performance under non-stationary conditions where drift behavior is prone to drastic changes. Moreover, the model's compact design guarantees compatibility with edge devices, allowing use in IoT-connected industrial systems. The paper also compares drift compensation techniques, showcasing benefits for self-calibration and adaptability. Sensitivity analysis indicates that the SCNN model remains robust to drift type, noise levels, and sensor type changes. Results show that integrating machine learning self-calibration into the sensor improved reliability, lowered maintenance costs, and enhanced system integrity in engineering applications. The research will focus on expanding the SCNN framework to multi-sensor fusion systems and optimizing hybrid designs.